Overview

Dataset statistics

Number of variables28
Number of observations280404
Missing cells2768016
Missing cells (%)35.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.4 MiB
Average record size in memory211.0 B

Variable types

Unsupported1
DateTime3
Categorical15
Numeric9

Warnings

Measure Period has constant value "2020Q1-2020Q4" Constant
Processing Date has constant value "2021-06-01 00:00:00" Constant
Provider Name has a high cardinality: 15044 distinct values High cardinality
Provider Address has a high cardinality: 15271 distinct values High cardinality
Provider City has a high cardinality: 5243 distinct values High cardinality
Provider State has a high cardinality: 53 distinct values High cardinality
Location has a high cardinality: 15289 distinct values High cardinality
Q1 Measure Score is highly correlated with Q2 Measure Score and 3 other fieldsHigh correlation
Footnote for Q1 Measure Score is highly correlated with Footnote for Q2 Measure Score and 3 other fieldsHigh correlation
Q2 Measure Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Q2 Measure Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Q3 Measure Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Q3 Measure Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Q4 Measure Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Q4 Measure Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Four Quarter Average Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Four Quarter Average Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Q1 Measure Score is highly correlated with Q2 Measure Score and 3 other fieldsHigh correlation
Footnote for Q1 Measure Score is highly correlated with Footnote for Q2 Measure Score and 3 other fieldsHigh correlation
Q2 Measure Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Q2 Measure Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Q3 Measure Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Q3 Measure Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Q4 Measure Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Q4 Measure Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Four Quarter Average Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Four Quarter Average Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Q1 Measure Score is highly correlated with Q2 Measure Score and 3 other fieldsHigh correlation
Footnote for Q1 Measure Score is highly correlated with Footnote for Q2 Measure Score and 3 other fieldsHigh correlation
Q2 Measure Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Q2 Measure Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Q3 Measure Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Q3 Measure Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Q4 Measure Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Q4 Measure Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Four Quarter Average Score is highly correlated with Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Four Quarter Average Score is highly correlated with Footnote for Q1 Measure Score and 3 other fieldsHigh correlation
Footnote for Q3 Measure Score is highly correlated with Footnote for Q4 Measure Score and 3 other fieldsHigh correlation
Provider Zip Code is highly correlated with Provider StateHigh correlation
Payment Denial Length in Days is highly correlated with Payment Denial Start DateHigh correlation
Measure Code is highly correlated with Four Quarter Average Score and 6 other fieldsHigh correlation
Four Quarter Average Score is highly correlated with Measure Code and 7 other fieldsHigh correlation
Footnote for Q4 Measure Score is highly correlated with Footnote for Q3 Measure Score and 3 other fieldsHigh correlation
Resident type is highly correlated with Measure Code and 6 other fieldsHigh correlation
Q1 Measure Score is highly correlated with Measure Code and 7 other fieldsHigh correlation
Footnote for Q1 Measure Score is highly correlated with Footnote for Q3 Measure Score and 4 other fieldsHigh correlation
Q2 Measure Score is highly correlated with Measure Code and 7 other fieldsHigh correlation
Measure Description is highly correlated with Measure Code and 7 other fieldsHigh correlation
Q3 Measure Score is highly correlated with Measure Code and 7 other fieldsHigh correlation
Q4 Measure Score is highly correlated with Measure Code and 7 other fieldsHigh correlation
Used in Quality Measure Five Star Rating is highly correlated with Four Quarter Average Score and 5 other fieldsHigh correlation
Provider State is highly correlated with Provider Zip CodeHigh correlation
Footnote for Q2 Measure Score is highly correlated with Footnote for Q3 Measure Score and 3 other fieldsHigh correlation
Payment Denial Start Date is highly correlated with Payment Denial Length in Days and 1 other fieldsHigh correlation
Footnote for Four Quarter Average Score is highly correlated with Footnote for Q3 Measure Score and 3 other fieldsHigh correlation
Footnote for Q3 Measure Score is highly correlated with Footnote for Q2 Measure Score and 4 other fieldsHigh correlation
Penalty Type is highly correlated with Provider State and 1 other fieldsHigh correlation
Used in Quality Measure Five Star Rating is highly correlated with Measure Period and 1 other fieldsHigh correlation
Provider State is highly correlated with Penalty Type and 1 other fieldsHigh correlation
Footnote for Q2 Measure Score is highly correlated with Footnote for Q3 Measure Score and 4 other fieldsHigh correlation
Footnote for Q4 Measure Score is highly correlated with Footnote for Q3 Measure Score and 4 other fieldsHigh correlation
Measure Period is highly correlated with Footnote for Q3 Measure Score and 9 other fieldsHigh correlation
Resident type is highly correlated with Measure Period and 1 other fieldsHigh correlation
Footnote for Four Quarter Average Score is highly correlated with Footnote for Q3 Measure Score and 4 other fieldsHigh correlation
Footnote for Q1 Measure Score is highly correlated with Footnote for Q3 Measure Score and 4 other fieldsHigh correlation
Measure Description is highly correlated with Used in Quality Measure Five Star Rating and 2 other fieldsHigh correlation
Penalty Date has 269056 (96.0%) missing values Missing
Penalty Type has 269056 (96.0%) missing values Missing
Fine Amount has 271036 (96.7%) missing values Missing
Payment Denial Start Date has 278424 (99.3%) missing values Missing
Payment Denial Length in Days has 278424 (99.3%) missing values Missing
Q1 Measure Score has 38495 (13.7%) missing values Missing
Footnote for Q1 Measure Score has 241909 (86.3%) missing values Missing
Q2 Measure Score has 42302 (15.1%) missing values Missing
Footnote for Q2 Measure Score has 238102 (84.9%) missing values Missing
Q3 Measure Score has 46964 (16.7%) missing values Missing
Footnote for Q3 Measure Score has 233440 (83.3%) missing values Missing
Q4 Measure Score has 49932 (17.8%) missing values Missing
Footnote for Q4 Measure Score has 230472 (82.2%) missing values Missing
Four Quarter Average Score has 12836 (4.6%) missing values Missing
Footnote for Four Quarter Average Score has 267568 (95.4%) missing values Missing
Provider Address is uniformly distributed Uniform
Measure Description is uniformly distributed Uniform
Used in Quality Measure Five Star Rating is uniformly distributed Uniform
Location is uniformly distributed Uniform
Federal Provider Number is an unsupported type, check if it needs cleaning or further analysis Unsupported
Q1 Measure Score has 42205 (15.1%) zeros Zeros
Q2 Measure Score has 41383 (14.8%) zeros Zeros
Q3 Measure Score has 41932 (15.0%) zeros Zeros
Q4 Measure Score has 42025 (15.0%) zeros Zeros
Four Quarter Average Score has 28025 (10.0%) zeros Zeros

Reproduction

Analysis started2021-09-23 03:48:39.497993
Analysis finished2021-09-23 03:49:35.753147
Duration56.26 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Federal Provider Number
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size4.3 MiB

Penalty Date
Date

MISSING

Distinct344
Distinct (%)3.0%
Missing269056
Missing (%)96.0%
Memory size4.3 MiB
Minimum2018-01-04 00:00:00
Maximum2021-03-18 00:00:00
2021-09-22T20:49:35.925018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:36.088605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Penalty Type
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing269056
Missing (%)96.0%
Memory size2.4 MiB
Fine
9368 
Payment Denial
1980 

Length

Max length14
Median length4
Mean length5.744800846
Min length4

Characters and Unicode

Total characters65192
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFine
2nd rowFine
3rd rowFine
4th rowFine
5th rowFine

Common Values

ValueCountFrequency (%)
Fine9368
 
3.3%
Payment Denial1980
 
0.7%
(Missing)269056
96.0%

Length

2021-09-22T20:49:36.338524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-22T20:49:36.410003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
fine9368
70.3%
denial1980
 
14.9%
payment1980
 
14.9%

Most occurring characters

ValueCountFrequency (%)
n13328
20.4%
e13328
20.4%
i11348
17.4%
F9368
14.4%
a3960
 
6.1%
P1980
 
3.0%
y1980
 
3.0%
m1980
 
3.0%
t1980
 
3.0%
1980
 
3.0%
Other values (2)3960
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter49884
76.5%
Uppercase Letter13328
 
20.4%
Space Separator1980
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n13328
26.7%
e13328
26.7%
i11348
22.7%
a3960
 
7.9%
y1980
 
4.0%
m1980
 
4.0%
t1980
 
4.0%
l1980
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
F9368
70.3%
P1980
 
14.9%
D1980
 
14.9%
Space Separator
ValueCountFrequency (%)
1980
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin63212
97.0%
Common1980
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n13328
21.1%
e13328
21.1%
i11348
18.0%
F9368
14.8%
a3960
 
6.3%
P1980
 
3.1%
y1980
 
3.1%
m1980
 
3.1%
t1980
 
3.1%
D1980
 
3.1%
Common
ValueCountFrequency (%)
1980
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII65192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n13328
20.4%
e13328
20.4%
i11348
17.4%
F9368
14.4%
a3960
 
6.1%
P1980
 
3.0%
y1980
 
3.0%
m1980
 
3.0%
t1980
 
3.0%
1980
 
3.0%
Other values (2)3960
 
6.1%

Fine Amount
Real number (ℝ≥0)

MISSING

Distinct255
Distinct (%)2.7%
Missing271036
Missing (%)96.7%
Infinite0
Infinite (%)0.0%
Mean20786.27989
Minimum650
Maximum236145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2021-09-22T20:49:36.497436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum650
5-th percentile650
Q11625
median9750
Q320192
95-th percentile99956
Maximum236145
Range235495
Interquartile range (IQR)18567

Descriptive statistics

Standard deviation35268.4078
Coefficient of variation (CV)1.696715717
Kurtosis13.78118416
Mean20786.27989
Median Absolute Deviation (MAD)8125
Skewness3.42938224
Sum194725870
Variance1243860588
MonotonicityNot monotonic
2021-09-22T20:49:36.632368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6501170
 
0.4%
975666
 
0.2%
9750486
 
0.2%
1300306
 
0.1%
5000270
 
0.1%
1625252
 
0.1%
3250198
 
0.1%
13905180
 
0.1%
7296162
 
0.1%
7157152
 
0.1%
Other values (245)5526
 
2.0%
(Missing)271036
96.7%
ValueCountFrequency (%)
6501170
0.4%
975666
0.2%
100036
 
< 0.1%
1300306
 
0.1%
1625252
 
0.1%
1950108
 
< 0.1%
227518
 
< 0.1%
260018
 
< 0.1%
3250198
 
0.1%
331818
 
< 0.1%
ValueCountFrequency (%)
23614518
< 0.1%
22984318
< 0.1%
22801518
< 0.1%
22152118
< 0.1%
21980518
< 0.1%
20705818
< 0.1%
17719018
< 0.1%
16474018
< 0.1%
15303018
< 0.1%
14784718
< 0.1%

Payment Denial Start Date
Date

HIGH CORRELATION
MISSING

Distinct99
Distinct (%)5.0%
Missing278424
Missing (%)99.3%
Memory size4.3 MiB
Minimum2018-07-11 00:00:00
Maximum2021-04-15 00:00:00
2021-09-22T20:49:36.775135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:36.922089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Payment Denial Length in Days
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct54
Distinct (%)2.7%
Missing278424
Missing (%)99.3%
Infinite0
Infinite (%)0.0%
Mean28.43636364
Minimum1
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2021-09-22T20:49:37.063891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median17
Q333
95-th percentile90
Maximum391
Range390
Interquartile range (IQR)26

Descriptive statistics

Standard deviation42.68598684
Coefficient of variation (CV)1.501105675
Kurtosis45.07127208
Mean28.43636364
Median Absolute Deviation (MAD)11.5
Skewness5.834802785
Sum56304
Variance1822.093472
MonotonicityNot monotonic
2021-09-22T20:49:37.212452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1144
 
0.1%
11108
 
< 0.1%
590
 
< 0.1%
790
 
< 0.1%
690
 
< 0.1%
372
 
< 0.1%
1472
 
< 0.1%
2772
 
< 0.1%
3372
 
< 0.1%
1354
 
< 0.1%
Other values (44)1116
 
0.4%
(Missing)278424
99.3%
ValueCountFrequency (%)
1144
0.1%
218
 
< 0.1%
372
< 0.1%
436
 
< 0.1%
590
< 0.1%
690
< 0.1%
790
< 0.1%
836
 
< 0.1%
954
 
< 0.1%
1018
 
< 0.1%
ValueCountFrequency (%)
39118
< 0.1%
11918
< 0.1%
11818
< 0.1%
9718
< 0.1%
9318
< 0.1%
9018
< 0.1%
7318
< 0.1%
7218
< 0.1%
6718
< 0.1%
6518
< 0.1%

Provider Name
Categorical

HIGH CARDINALITY

Distinct15044
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
MILLER'S MERRY MANOR
 
540
BENDER TERRACE OF LUBBOCK
 
198
ARDEN WOOD
 
198
MANORCARE HEALTH SERVICES
 
162
RANGER CARE CENTER
 
126
Other values (15039)
279180 

Length

Max length50
Median length28
Mean length29.70804981
Min length6

Characters and Unicode

Total characters8330256
Distinct characters50
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBURNS NURSING HOME, INC.
2nd rowBURNS NURSING HOME, INC.
3rd rowBURNS NURSING HOME, INC.
4th rowBURNS NURSING HOME, INC.
5th rowBURNS NURSING HOME, INC.

Common Values

ValueCountFrequency (%)
MILLER'S MERRY MANOR540
 
0.2%
BENDER TERRACE OF LUBBOCK198
 
0.1%
ARDEN WOOD198
 
0.1%
MANORCARE HEALTH SERVICES162
 
0.1%
RANGER CARE CENTER126
 
< 0.1%
BRIGHTPOINTE AT LYTLE LAKE126
 
< 0.1%
PARKVIEW CARE CENTER108
 
< 0.1%
WINDSOR PLACE108
 
< 0.1%
LITTLE SISTERS OF THE POOR108
 
< 0.1%
COMMUNITY CARE CENTER90
 
< 0.1%
Other values (15034)278640
99.4%

Length

2021-09-22T20:49:37.533015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
center106794
 
8.8%
care56970
 
4.7%
rehabilitation54522
 
4.5%
nursing51174
 
4.2%
and50202
 
4.1%
health45162
 
3.7%
37908
 
3.1%
of30798
 
2.5%
healthcare26010
 
2.1%
rehab25632
 
2.1%
Other values (7521)735282
60.2%

Most occurring characters

ValueCountFrequency (%)
E953424
11.4%
941958
11.3%
A750438
 
9.0%
R662022
 
7.9%
N635652
 
7.6%
T588312
 
7.1%
I529092
 
6.4%
L435312
 
5.2%
O413046
 
5.0%
C384426
 
4.6%
Other values (40)2036574
24.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter7312428
87.8%
Space Separator941958
 
11.3%
Other Punctuation55566
 
0.7%
Dash Punctuation15606
 
0.2%
Open Punctuation2052
 
< 0.1%
Close Punctuation1980
 
< 0.1%
Decimal Number630
 
< 0.1%
Final Punctuation36
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E953424
13.0%
A750438
10.3%
R662022
9.1%
N635652
8.7%
T588312
 
8.0%
I529092
 
7.2%
L435312
 
6.0%
O413046
 
5.6%
C384426
 
5.3%
H384012
 
5.3%
Other values (16)1576692
21.6%
Decimal Number
ValueCountFrequency (%)
1126
20.0%
2108
17.1%
090
14.3%
390
14.3%
472
11.4%
954
8.6%
736
 
5.7%
618
 
2.9%
518
 
2.9%
818
 
2.9%
Other Punctuation
ValueCountFrequency (%)
&29862
53.7%
,17766
32.0%
'3708
 
6.7%
.2142
 
3.9%
/1962
 
3.5%
"36
 
0.1%
#36
 
0.1%
:36
 
0.1%
@18
 
< 0.1%
Space Separator
ValueCountFrequency (%)
941958
100.0%
Dash Punctuation
ValueCountFrequency (%)
-15606
100.0%
Open Punctuation
ValueCountFrequency (%)
(2052
100.0%
Close Punctuation
ValueCountFrequency (%)
)1980
100.0%
Final Punctuation
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7312428
87.8%
Common1017828
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E953424
13.0%
A750438
10.3%
R662022
9.1%
N635652
8.7%
T588312
 
8.0%
I529092
 
7.2%
L435312
 
6.0%
O413046
 
5.6%
C384426
 
5.3%
H384012
 
5.3%
Other values (16)1576692
21.6%
Common
ValueCountFrequency (%)
941958
92.5%
&29862
 
2.9%
,17766
 
1.7%
-15606
 
1.5%
'3708
 
0.4%
.2142
 
0.2%
(2052
 
0.2%
)1980
 
0.2%
/1962
 
0.2%
1126
 
< 0.1%
Other values (14)666
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8330220
> 99.9%
Punctuation36
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E953424
11.4%
941958
11.3%
A750438
 
9.0%
R662022
 
7.9%
N635652
 
7.6%
T588312
 
7.1%
I529092
 
6.4%
L435312
 
5.2%
O413046
 
5.0%
C384426
 
4.6%
Other values (39)2036538
24.4%
Punctuation
ValueCountFrequency (%)
36
100.0%

Provider Address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct15271
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
4510 27TH ST
 
198
8810 LONG POINT DR
 
198
1201 CLARKS DR
 
126
460 W MAIN ST
 
126
507 E W M WATSON BLVD
 
108
Other values (15266)
279648 

Length

Max length50
Median length18
Mean length18.73507511
Min length7

Characters and Unicode

Total characters5253390
Distinct characters54
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row701 MONROE STREET NW
2nd row701 MONROE STREET NW
3rd row701 MONROE STREET NW
4th row701 MONROE STREET NW
5th row701 MONROE STREET NW

Common Values

ValueCountFrequency (%)
4510 27TH ST198
 
0.1%
8810 LONG POINT DR198
 
0.1%
1201 CLARKS DR126
 
< 0.1%
460 W MAIN ST126
 
< 0.1%
507 E W M WATSON BLVD108
 
< 0.1%
3000 MOCKINGBIRD LN90
 
< 0.1%
11830 NORTHPOINTE BOULEVARD90
 
< 0.1%
1260 ABRAMS RD90
 
< 0.1%
6211 S NEW BRAUNFELS AVE90
 
< 0.1%
3640 HAMPTON DR90
 
< 0.1%
Other values (15261)279198
99.6%

Length

2021-09-22T20:49:38.083556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street53280
 
5.3%
road39564
 
4.0%
avenue31896
 
3.2%
drive25326
 
2.5%
st23436
 
2.3%
ave16686
 
1.7%
rd15606
 
1.6%
north15048
 
1.5%
west14562
 
1.5%
south14130
 
1.4%
Other values (10672)748962
75.0%

Most occurring characters

ValueCountFrequency (%)
721566
 
13.7%
E472860
 
9.0%
R330714
 
6.3%
T320490
 
6.1%
A304254
 
5.8%
S239454
 
4.6%
O226332
 
4.3%
N224172
 
4.3%
0218574
 
4.2%
1208098
 
4.0%
Other values (44)1986876
37.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3453192
65.7%
Decimal Number1061388
 
20.2%
Space Separator721566
 
13.7%
Other Punctuation14904
 
0.3%
Dash Punctuation2106
 
< 0.1%
Lowercase Letter162
 
< 0.1%
Open Punctuation36
 
< 0.1%
Close Punctuation36
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E472860
13.7%
R330714
 
9.6%
T320490
 
9.3%
A304254
 
8.8%
S239454
 
6.9%
O226332
 
6.6%
N224172
 
6.5%
D173700
 
5.0%
I160758
 
4.7%
L156114
 
4.5%
Other values (16)844344
24.5%
Decimal Number
ValueCountFrequency (%)
0218574
20.6%
1208098
19.6%
2124758
11.8%
5111330
10.5%
392970
8.8%
478102
 
7.4%
663000
 
5.9%
758968
 
5.6%
952866
 
5.0%
852722
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
a36
22.2%
i18
11.1%
r18
11.1%
b18
11.1%
n18
11.1%
k18
11.1%
s18
11.1%
d18
11.1%
Other Punctuation
ValueCountFrequency (%)
.9576
64.3%
,3960
26.6%
'522
 
3.5%
#360
 
2.4%
/270
 
1.8%
&216
 
1.4%
Space Separator
ValueCountFrequency (%)
721566
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2106
100.0%
Open Punctuation
ValueCountFrequency (%)
(36
100.0%
Close Punctuation
ValueCountFrequency (%)
)36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3453354
65.7%
Common1800036
34.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
E472860
13.7%
R330714
 
9.6%
T320490
 
9.3%
A304254
 
8.8%
S239454
 
6.9%
O226332
 
6.6%
N224172
 
6.5%
D173700
 
5.0%
I160758
 
4.7%
L156114
 
4.5%
Other values (24)844506
24.5%
Common
ValueCountFrequency (%)
721566
40.1%
0218574
 
12.1%
1208098
 
11.6%
2124758
 
6.9%
5111330
 
6.2%
392970
 
5.2%
478102
 
4.3%
663000
 
3.5%
758968
 
3.3%
952866
 
2.9%
Other values (10)69804
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII5253390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
721566
 
13.7%
E472860
 
9.0%
R330714
 
6.3%
T320490
 
6.1%
A304254
 
5.8%
S239454
 
4.6%
O226332
 
4.3%
N224172
 
4.3%
0218574
 
4.2%
1208098
 
4.0%
Other values (44)1986876
37.8%

Provider City
Categorical

HIGH CARDINALITY

Distinct5243
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
HOUSTON
 
1962
LOS ANGELES
 
1422
CHICAGO
 
1422
SAN ANTONIO
 
1332
CINCINNATI
 
1296
Other values (5238)
272970 

Length

Max length20
Median length8
Mean length8.770766466
Min length3

Characters and Unicode

Total characters2459358
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRUSSELLVILLE
2nd rowRUSSELLVILLE
3rd rowRUSSELLVILLE
4th rowRUSSELLVILLE
5th rowRUSSELLVILLE

Common Values

ValueCountFrequency (%)
HOUSTON1962
 
0.7%
LOS ANGELES1422
 
0.5%
CHICAGO1422
 
0.5%
SAN ANTONIO1332
 
0.5%
CINCINNATI1296
 
0.5%
COLUMBUS1026
 
0.4%
DALLAS936
 
0.3%
LOUISVILLE936
 
0.3%
INDIANAPOLIS900
 
0.3%
SPRINGFIELD900
 
0.3%
Other values (5233)268272
95.7%

Length

2021-09-22T20:49:38.388254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
city7650
 
2.2%
san3726
 
1.1%
new2970
 
0.8%
saint2772
 
0.8%
fort2538
 
0.7%
west2466
 
0.7%
beach2430
 
0.7%
park2394
 
0.7%
lake2286
 
0.7%
houston1962
 
0.6%
Other values (4628)318708
91.1%

Most occurring characters

ValueCountFrequency (%)
E236772
 
9.6%
A228366
 
9.3%
N201744
 
8.2%
O195912
 
8.0%
L192330
 
7.8%
R171072
 
7.0%
I160218
 
6.5%
T140760
 
5.7%
S139986
 
5.7%
C81540
 
3.3%
Other values (19)710658
28.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2389590
97.2%
Space Separator69498
 
2.8%
Dash Punctuation162
 
< 0.1%
Other Punctuation108
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E236772
 
9.9%
A228366
 
9.6%
N201744
 
8.4%
O195912
 
8.2%
L192330
 
8.0%
R171072
 
7.2%
I160218
 
6.7%
T140760
 
5.9%
S139986
 
5.9%
C81540
 
3.4%
Other values (16)640890
26.8%
Space Separator
ValueCountFrequency (%)
69498
100.0%
Other Punctuation
ValueCountFrequency (%)
'108
100.0%
Dash Punctuation
ValueCountFrequency (%)
-162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2389590
97.2%
Common69768
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E236772
 
9.9%
A228366
 
9.6%
N201744
 
8.4%
O195912
 
8.2%
L192330
 
8.0%
R171072
 
7.2%
I160218
 
6.7%
T140760
 
5.9%
S139986
 
5.9%
C81540
 
3.4%
Other values (16)640890
26.8%
Common
ValueCountFrequency (%)
69498
99.6%
-162
 
0.2%
'108
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2459358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E236772
 
9.6%
A228366
 
9.3%
N201744
 
8.2%
O195912
 
8.0%
L192330
 
7.8%
R171072
 
7.0%
I160218
 
6.5%
T140760
 
5.7%
S139986
 
5.7%
C81540
 
3.3%
Other values (19)710658
28.9%

Provider State
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
TX
26766 
CA
21294 
OH
 
17172
IL
 
12744
FL
 
12672
Other values (48)
189756 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters560808
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAL
2nd rowAL
3rd rowAL
4th rowAL
5th rowAL

Common Values

ValueCountFrequency (%)
TX26766
 
9.5%
CA21294
 
7.6%
OH17172
 
6.1%
IL12744
 
4.5%
FL12672
 
4.5%
PA12366
 
4.4%
NY11088
 
4.0%
IN9594
 
3.4%
MO9288
 
3.3%
MI7830
 
2.8%
Other values (43)139590
49.8%

Length

2021-09-22T20:49:38.682107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx26766
 
9.5%
ca21294
 
7.6%
oh17172
 
6.1%
il12744
 
4.5%
fl12672
 
4.5%
pa12366
 
4.4%
ny11088
 
4.0%
in9594
 
3.4%
mo9288
 
3.3%
mi7830
 
2.8%
Other values (43)139590
49.8%

Most occurring characters

ValueCountFrequency (%)
A79416
14.2%
N55710
9.9%
I47826
 
8.5%
M42264
 
7.5%
C40518
 
7.2%
T39798
 
7.1%
O38214
 
6.8%
L34488
 
6.1%
X26766
 
4.8%
H19260
 
3.4%
Other values (14)136548
24.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter560808
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A79416
14.2%
N55710
9.9%
I47826
 
8.5%
M42264
 
7.5%
C40518
 
7.2%
T39798
 
7.1%
O38214
 
6.8%
L34488
 
6.1%
X26766
 
4.8%
H19260
 
3.4%
Other values (14)136548
24.3%

Most occurring scripts

ValueCountFrequency (%)
Latin560808
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A79416
14.2%
N55710
9.9%
I47826
 
8.5%
M42264
 
7.5%
C40518
 
7.2%
T39798
 
7.1%
O38214
 
6.8%
L34488
 
6.1%
X26766
 
4.8%
H19260
 
3.4%
Other values (14)136548
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII560808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A79416
14.2%
N55710
9.9%
I47826
 
8.5%
M42264
 
7.5%
C40518
 
7.2%
T39798
 
7.1%
O38214
 
6.8%
L34488
 
6.1%
X26766
 
4.8%
H19260
 
3.4%
Other values (14)136548
24.3%

Provider Zip Code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9181
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50784.27789
Minimum660
Maximum99929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2021-09-22T20:49:38.795419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum660
5-th percentile6471
Q130120
median49507
Q374133
95-th percentile94109
Maximum99929
Range99269
Interquartile range (IQR)44013

Descriptive statistics

Standard deviation26843.76914
Coefficient of variation (CV)0.5285842441
Kurtosis-1.006868665
Mean50784.27789
Median Absolute Deviation (MAD)21598.5
Skewness-0.04423245
Sum1.424011466 × 1010
Variance720587941.7
MonotonicityNot monotonic
2021-09-22T20:49:38.941017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79410270
 
0.1%
77055234
 
0.1%
94541216
 
0.1%
11691198
 
0.1%
63017180
 
0.1%
33161162
 
0.1%
85712162
 
0.1%
77493162
 
0.1%
91103162
 
0.1%
21228162
 
0.1%
Other values (9171)278496
99.3%
ValueCountFrequency (%)
66018
 
< 0.1%
69318
 
< 0.1%
71718
 
< 0.1%
79218
 
< 0.1%
92618
 
< 0.1%
92818
 
< 0.1%
100190
< 0.1%
100218
 
< 0.1%
101336
 
< 0.1%
102018
 
< 0.1%
ValueCountFrequency (%)
9992918
< 0.1%
9990118
< 0.1%
9983518
< 0.1%
9983318
< 0.1%
9980118
< 0.1%
9976218
< 0.1%
9975218
< 0.1%
9970118
< 0.1%
9968618
< 0.1%
9966918
< 0.1%

Measure Code
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean427.8333333
Minimum401
Maximum472
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2021-09-22T20:49:39.063939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum401
5-th percentile401
Q1407
median417
Q3452
95-th percentile472
Maximum472
Range71
Interquartile range (IQR)45

Descriptive statistics

Standard deviation23.94964256
Coefficient of variation (CV)0.05597890743
Kurtosis-1.189569187
Mean427.8333333
Median Absolute Deviation (MAD)13
Skewness0.5664715388
Sum119966178
Variance573.5853789
MonotonicityNot monotonic
2021-09-22T20:49:39.174217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
40115578
 
5.6%
40415578
 
5.6%
47115578
 
5.6%
45415578
 
5.6%
45315578
 
5.6%
45215578
 
5.6%
45115578
 
5.6%
43415578
 
5.6%
43015578
 
5.6%
41915578
 
5.6%
Other values (8)124624
44.4%
ValueCountFrequency (%)
40115578
5.6%
40415578
5.6%
40515578
5.6%
40615578
5.6%
40715578
5.6%
40815578
5.6%
40915578
5.6%
41015578
5.6%
41515578
5.6%
41915578
5.6%
ValueCountFrequency (%)
47215578
5.6%
47115578
5.6%
45415578
5.6%
45315578
5.6%
45215578
5.6%
45115578
5.6%
43415578
5.6%
43015578
5.6%
41915578
5.6%
41515578
5.6%

Measure Description
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Percentage of long-stay residents who lose too much weight
 
15578
Percentage of long-stay residents whose need for help with daily activities has increased
 
15578
Percentage of long-stay residents who received an antianxiety or hypnotic medication
 
15578
Percentage of long-stay residents whose ability to move independently worsened
 
15578
Percentage of long-stay residents who have depressive symptoms
 
15578
Other values (13)
202514 

Length

Max length107
Median length81.5
Mean length79.16666667
Min length58

Characters and Unicode

Total characters22198650
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPercentage of long-stay residents whose need for help with daily activities has increased
2nd rowPercentage of long-stay residents who lose too much weight
3rd rowPercentage of low risk long-stay residents who lose control of their bowels or bladder
4th rowPercentage of long-stay residents with a catheter inserted and left in their bladder
5th rowPercentage of long-stay residents with a urinary tract infection

Common Values

ValueCountFrequency (%)
Percentage of long-stay residents who lose too much weight15578
 
5.6%
Percentage of long-stay residents whose need for help with daily activities has increased15578
 
5.6%
Percentage of long-stay residents who received an antianxiety or hypnotic medication15578
 
5.6%
Percentage of long-stay residents whose ability to move independently worsened15578
 
5.6%
Percentage of long-stay residents who have depressive symptoms15578
 
5.6%
Percentage of long-stay residents assessed and appropriately given the seasonal influenza vaccine15578
 
5.6%
Percentage of short-stay residents who newly received an antipsychotic medication15578
 
5.6%
Percentage of long-stay residents experiencing one or more falls with major injury15578
 
5.6%
Percentage of short-stay residents who were assessed and appropriately given the seasonal influenza vaccine15578
 
5.6%
Percentage of high risk long-stay residents with pressure ulcers15578
 
5.6%
Other values (8)124624
44.4%

Length

2021-09-22T20:49:39.440209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of295982
 
9.9%
percentage280404
 
9.4%
residents280404
 
9.4%
long-stay218092
 
7.3%
who140202
 
4.7%
and77890
 
2.6%
with77890
 
2.6%
appropriately62312
 
2.1%
given62312
 
2.1%
the62312
 
2.1%
Other values (63)1433176
47.9%

Most occurring characters

ValueCountFrequency (%)
e2850774
12.8%
2710572
12.2%
n1620112
 
7.3%
s1604534
 
7.2%
t1588956
 
7.2%
a1448754
 
6.5%
o1339708
 
6.0%
r1277396
 
5.8%
i1261818
 
5.7%
c841212
 
3.8%
Other values (18)5654814
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18927270
85.3%
Space Separator2710572
 
12.2%
Uppercase Letter280404
 
1.3%
Dash Punctuation280404
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2850774
15.1%
n1620112
 
8.6%
s1604534
 
8.5%
t1588956
 
8.4%
a1448754
 
7.7%
o1339708
 
7.1%
r1277396
 
6.7%
i1261818
 
6.7%
c841212
 
4.4%
d732166
 
3.9%
Other values (15)4361840
23.0%
Uppercase Letter
ValueCountFrequency (%)
P280404
100.0%
Space Separator
ValueCountFrequency (%)
2710572
100.0%
Dash Punctuation
ValueCountFrequency (%)
-280404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19207674
86.5%
Common2990976
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2850774
14.8%
n1620112
 
8.4%
s1604534
 
8.4%
t1588956
 
8.3%
a1448754
 
7.5%
o1339708
 
7.0%
r1277396
 
6.7%
i1261818
 
6.6%
c841212
 
4.4%
d732166
 
3.8%
Other values (16)4642244
24.2%
Common
ValueCountFrequency (%)
2710572
90.6%
-280404
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII22198650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2850774
12.8%
2710572
12.2%
n1620112
 
7.3%
s1604534
 
7.2%
t1588956
 
7.2%
a1448754
 
6.5%
o1339708
 
6.0%
r1277396
 
5.8%
i1261818
 
5.7%
c841212
 
3.8%
Other values (18)5654814
25.5%

Resident type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
Long Stay
218092 
Short Stay
62312 

Length

Max length10
Median length9
Mean length9.222222222
Min length9

Characters and Unicode

Total characters2585948
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLong Stay
2nd rowLong Stay
3rd rowLong Stay
4th rowLong Stay
5th rowLong Stay

Common Values

ValueCountFrequency (%)
Long Stay218092
77.8%
Short Stay62312
 
22.2%

Length

2021-09-22T20:49:39.690598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-22T20:49:39.769245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
stay280404
50.0%
long218092
38.9%
short62312
 
11.1%

Most occurring characters

ValueCountFrequency (%)
S342716
13.3%
t342716
13.3%
o280404
10.8%
280404
10.8%
a280404
10.8%
y280404
10.8%
L218092
8.4%
n218092
8.4%
g218092
8.4%
h62312
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1744736
67.5%
Uppercase Letter560808
 
21.7%
Space Separator280404
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t342716
19.6%
o280404
16.1%
a280404
16.1%
y280404
16.1%
n218092
12.5%
g218092
12.5%
h62312
 
3.6%
r62312
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
S342716
61.1%
L218092
38.9%
Space Separator
ValueCountFrequency (%)
280404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2305544
89.2%
Common280404
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
S342716
14.9%
t342716
14.9%
o280404
12.2%
a280404
12.2%
y280404
12.2%
L218092
9.5%
n218092
9.5%
g218092
9.5%
h62312
 
2.7%
r62312
 
2.7%
Common
ValueCountFrequency (%)
280404
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2585948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S342716
13.3%
t342716
13.3%
o280404
10.8%
280404
10.8%
a280404
10.8%
y280404
10.8%
L218092
8.4%
n218092
8.4%
g218092
8.4%
h62312
 
2.4%

Q1 Measure Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct42920
Distinct (%)17.7%
Missing38495
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean30.66815818
Minimum0
Maximum100
Zeros42205
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2021-09-22T20:49:39.872383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.12766
median10.71429
Q363.07692
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)60.94926

Descriptive statistics

Standard deviation37.11705779
Coefficient of variation (CV)1.210279978
Kurtosis-0.8112827491
Mean30.66815818
Median Absolute Deviation (MAD)10.71429
Skewness0.9432812526
Sum7418903.476
Variance1377.675979
MonotonicityNot monotonic
2021-09-22T20:49:40.023684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
042205
 
15.1%
10015953
 
5.7%
10863
 
0.3%
14.28571818
 
0.3%
8.33333782
 
0.3%
7.69231770
 
0.3%
9.09091750
 
0.3%
12.5744
 
0.3%
4.7619725
 
0.3%
16.66667725
 
0.3%
Other values (42910)177574
63.3%
(Missing)38495
 
13.7%
ValueCountFrequency (%)
042205
15.1%
0.134411
 
< 0.1%
0.16261
 
< 0.1%
0.1670231
 
< 0.1%
0.1705571
 
< 0.1%
0.202841
 
< 0.1%
0.2139211
 
< 0.1%
0.2147091
 
< 0.1%
0.217391
 
< 0.1%
0.218821
 
< 0.1%
ValueCountFrequency (%)
10015953
5.7%
99.864131
 
< 0.1%
99.854861
 
< 0.1%
99.843262
 
< 0.1%
99.836871
 
< 0.1%
99.8144711
 
< 0.1%
99.811682
 
< 0.1%
99.8113211
 
< 0.1%
99.809891
 
< 0.1%
99.8095241
 
< 0.1%

Footnote for Q1 Measure Score
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing241909
Missing (%)86.3%
Memory size4.3 MiB
9.0
36965 
10.0
 
1530

Length

Max length4
Median length3
Mean length3.039745421
Min length3

Characters and Unicode

Total characters117015
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.036965
 
13.2%
10.01530
 
0.5%
(Missing)241909
86.3%

Length

2021-09-22T20:49:40.276000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-22T20:49:40.353808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
9.036965
96.0%
10.01530
 
4.0%

Most occurring characters

ValueCountFrequency (%)
040025
34.2%
.38495
32.9%
936965
31.6%
11530
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number78520
67.1%
Other Punctuation38495
32.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
040025
51.0%
936965
47.1%
11530
 
1.9%
Other Punctuation
ValueCountFrequency (%)
.38495
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common117015
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
040025
34.2%
.38495
32.9%
936965
31.6%
11530
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII117015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
040025
34.2%
.38495
32.9%
936965
31.6%
11530
 
1.3%

Q2 Measure Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct41092
Distinct (%)17.3%
Missing42302
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean31.20409104
Minimum0
Maximum100
Zeros41383
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2021-09-22T20:49:40.442679image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.27273
median11.9403
Q362.5
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)60.22727

Descriptive statistics

Standard deviation36.95947342
Coefficient of variation (CV)1.184443199
Kurtosis-0.819722511
Mean31.20409104
Median Absolute Deviation (MAD)11.9403
Skewness0.926289693
Sum7429756.484
Variance1366.002676
MonotonicityNot monotonic
2021-09-22T20:49:40.588957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041383
 
14.8%
10016269
 
5.8%
10965
 
0.3%
14.28571869
 
0.3%
12.5807
 
0.3%
8.33333785
 
0.3%
16.66667765
 
0.3%
11.11111758
 
0.3%
20750
 
0.3%
4.34783747
 
0.3%
Other values (41082)174004
62.1%
(Missing)42302
 
15.1%
ValueCountFrequency (%)
041383
14.8%
0.133871
 
< 0.1%
0.1675831
 
< 0.1%
0.169781
 
< 0.1%
0.172121
 
< 0.1%
0.1803961
 
< 0.1%
0.207041
 
< 0.1%
0.2119521
 
< 0.1%
0.213681
 
< 0.1%
0.229361
 
< 0.1%
ValueCountFrequency (%)
10016269
5.8%
99.844481
 
< 0.1%
99.827881
 
< 0.1%
99.821111
 
< 0.1%
99.8144711
 
< 0.1%
99.8113211
 
< 0.1%
99.8095241
 
< 0.1%
99.806951
 
< 0.1%
99.801591
 
< 0.1%
99.797161
 
< 0.1%

Footnote for Q2 Measure Score
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing238102
Missing (%)84.9%
Memory size4.3 MiB
9.0
41154 
10.0
 
1148

Length

Max length4
Median length3
Mean length3.027138197
Min length3

Characters and Unicode

Total characters128054
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.041154
 
14.7%
10.01148
 
0.4%
(Missing)238102
84.9%

Length

2021-09-22T20:49:40.861229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-22T20:49:40.944549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
9.041154
97.3%
10.01148
 
2.7%

Most occurring characters

ValueCountFrequency (%)
043450
33.9%
.42302
33.0%
941154
32.1%
11148
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number85752
67.0%
Other Punctuation42302
33.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
043450
50.7%
941154
48.0%
11148
 
1.3%
Other Punctuation
ValueCountFrequency (%)
.42302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common128054
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
043450
33.9%
.42302
33.0%
941154
32.1%
11148
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII128054
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
043450
33.9%
.42302
33.0%
941154
32.1%
11148
 
0.9%

Q3 Measure Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct38292
Distinct (%)16.4%
Missing46964
Missing (%)16.7%
Infinite0
Infinite (%)0.0%
Mean30.97604272
Minimum0
Maximum100
Zeros41932
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2021-09-22T20:49:41.045424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.29007875
median11.90476
Q361.29032
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)59.00024125

Descriptive statistics

Standard deviation36.85429636
Coefficient of variation (CV)1.189767741
Kurtosis-0.7880609993
Mean30.97604272
Median Absolute Deviation (MAD)11.90476
Skewness0.9426342918
Sum7231047.413
Variance1358.23916
MonotonicityNot monotonic
2021-09-22T20:49:41.190402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041932
 
15.0%
10015906
 
5.7%
10918
 
0.3%
14.28571911
 
0.3%
12.5883
 
0.3%
9.09091849
 
0.3%
8.33333826
 
0.3%
16.66667807
 
0.3%
11.11111800
 
0.3%
20769
 
0.3%
Other values (38282)168839
60.2%
(Missing)46964
 
16.7%
ValueCountFrequency (%)
041932
15.0%
0.139281
 
< 0.1%
0.19961
 
< 0.1%
0.234741
 
< 0.1%
0.2358691
 
< 0.1%
0.2417071
 
< 0.1%
0.2436561
 
< 0.1%
0.246911
 
< 0.1%
0.247521
 
< 0.1%
0.250631
 
< 0.1%
ValueCountFrequency (%)
10015906
5.7%
99.854011
 
< 0.1%
99.8144711
 
< 0.1%
99.8113211
 
< 0.1%
99.8095241
 
< 0.1%
99.79921
 
< 0.1%
99.7950821
 
< 0.1%
99.785871
 
< 0.1%
99.7767861
 
< 0.1%
99.775781
 
< 0.1%

Footnote for Q3 Measure Score
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing233440
Missing (%)83.3%
Memory size4.3 MiB
9.0
46008 
10.0
 
956

Length

Max length4
Median length3
Mean length3.020356017
Min length3

Characters and Unicode

Total characters141848
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.046008
 
16.4%
10.0956
 
0.3%
(Missing)233440
83.3%

Length

2021-09-22T20:49:41.427084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-22T20:49:41.499802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
9.046008
98.0%
10.0956
 
2.0%

Most occurring characters

ValueCountFrequency (%)
047920
33.8%
.46964
33.1%
946008
32.4%
1956
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number94884
66.9%
Other Punctuation46964
33.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
047920
50.5%
946008
48.5%
1956
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.46964
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common141848
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
047920
33.8%
.46964
33.1%
946008
32.4%
1956
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII141848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
047920
33.8%
.46964
33.1%
946008
32.4%
1956
 
0.7%

Q4 Measure Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct37922
Distinct (%)16.5%
Missing49932
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean31.43156472
Minimum0
Maximum100
Zeros42025
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2021-09-22T20:49:41.588252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.29645475
median12.5
Q362.889088
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)60.59263325

Descriptive statistics

Standard deviation36.88881258
Coefficient of variation (CV)1.173623168
Kurtosis-0.839584725
Mean31.43156472
Median Absolute Deviation (MAD)12.5
Skewness0.9100430839
Sum7244095.585
Variance1360.784493
MonotonicityNot monotonic
2021-09-22T20:49:41.728469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
042025
 
15.0%
10015418
 
5.5%
10948
 
0.3%
14.28571931
 
0.3%
12.5885
 
0.3%
9.09091877
 
0.3%
16.66667872
 
0.3%
20817
 
0.3%
8.33333791
 
0.3%
11.11111783
 
0.3%
Other values (37912)166125
59.2%
(Missing)49932
 
17.8%
ValueCountFrequency (%)
042025
15.0%
0.141041
 
< 0.1%
0.1493061
 
< 0.1%
0.166111
 
< 0.1%
0.1874481
 
< 0.1%
0.191571
 
< 0.1%
0.195691
 
< 0.1%
0.209641
 
< 0.1%
0.2246271
 
< 0.1%
0.227791
 
< 0.1%
ValueCountFrequency (%)
10015418
5.5%
99.862071
 
< 0.1%
99.8144711
 
< 0.1%
99.8113211
 
< 0.1%
99.8095241
 
< 0.1%
99.7950821
 
< 0.1%
99.793391
 
< 0.1%
99.783551
 
< 0.1%
99.7767861
 
< 0.1%
99.7737561
 
< 0.1%

Footnote for Q4 Measure Score
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing230472
Missing (%)82.2%
Memory size4.3 MiB
9.0
49246 
10.0
 
686

Length

Max length4
Median length3
Mean length3.013738685
Min length3

Characters and Unicode

Total characters150482
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.049246
 
17.6%
10.0686
 
0.2%
(Missing)230472
82.2%

Length

2021-09-22T20:49:41.964066image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-22T20:49:42.039943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
9.049246
98.6%
10.0686
 
1.4%

Most occurring characters

ValueCountFrequency (%)
050618
33.6%
.49932
33.2%
949246
32.7%
1686
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number100550
66.8%
Other Punctuation49932
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
050618
50.3%
949246
49.0%
1686
 
0.7%
Other Punctuation
ValueCountFrequency (%)
.49932
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common150482
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
050618
33.6%
.49932
33.2%
949246
32.7%
1686
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII150482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
050618
33.6%
.49932
33.2%
949246
32.7%
1686
 
0.5%

Four Quarter Average Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct114504
Distinct (%)42.8%
Missing12836
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean31.87993993
Minimum0
Maximum100
Zeros28025
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2021-09-22T20:49:42.134197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.631579
median13.0519075
Q364.37413525
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)61.74255625

Descriptive statistics

Standard deviation36.50061441
Coefficient of variation (CV)1.144939874
Kurtosis-0.8783855388
Mean31.87993993
Median Absolute Deviation (MAD)12.42199
Skewness0.8811712984
Sum8530051.768
Variance1332.294852
MonotonicityNot monotonic
2021-09-22T20:49:42.277830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
028025
 
10.0%
10013702
 
4.9%
85.714286187
 
0.1%
87.5187
 
0.1%
83.333333186
 
0.1%
80178
 
0.1%
90177
 
0.1%
66.666667165
 
0.1%
88.888889152
 
0.1%
93.333333143
 
0.1%
Other values (114494)224466
80.1%
(Missing)12836
 
4.6%
ValueCountFrequency (%)
028025
10.0%
0.040421
 
< 0.1%
0.0509291
 
< 0.1%
0.0567221
 
< 0.1%
0.0578911
 
< 0.1%
0.0613491
 
< 0.1%
0.0668011
 
< 0.1%
0.0677041
 
< 0.1%
0.0698011
 
< 0.1%
0.0704721
 
< 0.1%
ValueCountFrequency (%)
10013702
4.9%
99.9444741
 
< 0.1%
99.934771
 
< 0.1%
99.9262541
 
< 0.1%
99.9186331
 
< 0.1%
99.9103151
 
< 0.1%
99.9084251
 
< 0.1%
99.9081741
 
< 0.1%
99.906191
 
< 0.1%
99.902631
 
< 0.1%

Footnote for Four Quarter Average Score
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing267568
Missing (%)95.4%
Memory size4.3 MiB
9.0
12390 
10.0
 
446

Length

Max length4
Median length3
Mean length3.034746027
Min length3

Characters and Unicode

Total characters38954
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row9.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.012390
 
4.4%
10.0446
 
0.2%
(Missing)267568
95.4%

Length

2021-09-22T20:49:42.835446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-22T20:49:42.904535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
9.012390
96.5%
10.0446
 
3.5%

Most occurring characters

ValueCountFrequency (%)
013282
34.1%
.12836
33.0%
912390
31.8%
1446
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26118
67.0%
Other Punctuation12836
33.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013282
50.9%
912390
47.4%
1446
 
1.7%
Other Punctuation
ValueCountFrequency (%)
.12836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common38954
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013282
34.1%
.12836
33.0%
912390
31.8%
1446
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII38954
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013282
34.1%
.12836
33.0%
912390
31.8%
1446
 
1.1%

Used in Quality Measure Five Star Rating
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
N
140202 
Y
140202 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters280404
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowN
3rd rowN
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
N140202
50.0%
Y140202
50.0%

Length

2021-09-22T20:49:43.104563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-22T20:49:43.177735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
n140202
50.0%
y140202
50.0%

Most occurring characters

ValueCountFrequency (%)
Y140202
50.0%
N140202
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter280404
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y140202
50.0%
N140202
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin280404
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y140202
50.0%
N140202
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII280404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y140202
50.0%
N140202
50.0%

Measure Period
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
2020Q1-2020Q4
280404 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters3645252
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020Q1-2020Q4
2nd row2020Q1-2020Q4
3rd row2020Q1-2020Q4
4th row2020Q1-2020Q4
5th row2020Q1-2020Q4

Common Values

ValueCountFrequency (%)
2020Q1-2020Q4280404
100.0%

Length

2021-09-22T20:49:43.357246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-22T20:49:43.431244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
2020q1-2020q4280404
100.0%

Most occurring characters

ValueCountFrequency (%)
21121616
30.8%
01121616
30.8%
Q560808
15.4%
1280404
 
7.7%
-280404
 
7.7%
4280404
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2804040
76.9%
Uppercase Letter560808
 
15.4%
Dash Punctuation280404
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
21121616
40.0%
01121616
40.0%
1280404
 
10.0%
4280404
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
Q560808
100.0%
Dash Punctuation
ValueCountFrequency (%)
-280404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3084444
84.6%
Latin560808
 
15.4%

Most frequent character per script

Common
ValueCountFrequency (%)
21121616
36.4%
01121616
36.4%
1280404
 
9.1%
-280404
 
9.1%
4280404
 
9.1%
Latin
ValueCountFrequency (%)
Q560808
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3645252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21121616
30.8%
01121616
30.8%
Q560808
15.4%
1280404
 
7.7%
-280404
 
7.7%
4280404
 
7.7%

Location
Categorical

HIGH CARDINALITY
UNIFORM

Distinct15289
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
4510 27TH ST, LUBBOCK, TX, 79410
 
198
8810 LONG POINT DR, HOUSTON, TX, 77055
 
198
1201 CLARKS DR, ABILENE, TX, 79602
 
126
460 W MAIN ST, RANGER, TX, 76470
 
126
507 E W M WATSON BLVD, DAINGERFIELD, TX, 75638
 
108
Other values (15284)
279648 

Length

Max length75
Median length40
Mean length40.50584157
Min length26

Characters and Unicode

Total characters11358000
Distinct characters54
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row701 MONROE STREET NW, RUSSELLVILLE, AL, 35653
2nd row701 MONROE STREET NW, RUSSELLVILLE, AL, 35653
3rd row701 MONROE STREET NW, RUSSELLVILLE, AL, 35653
4th row701 MONROE STREET NW, RUSSELLVILLE, AL, 35653
5th row701 MONROE STREET NW, RUSSELLVILLE, AL, 35653

Common Values

ValueCountFrequency (%)
4510 27TH ST, LUBBOCK, TX, 79410198
 
0.1%
8810 LONG POINT DR, HOUSTON, TX, 77055198
 
0.1%
1201 CLARKS DR, ABILENE, TX, 79602126
 
< 0.1%
460 W MAIN ST, RANGER, TX, 76470126
 
< 0.1%
507 E W M WATSON BLVD, DAINGERFIELD, TX, 75638108
 
< 0.1%
25150 LAKECREST MANOR DR, KATY, TX, 7749390
 
< 0.1%
3640 HAMPTON DR, MISSOURI CITY, TX, 7745990
 
< 0.1%
11830 NORTHPOINTE BOULEVARD, TOMBALL, TX, 7737790
 
< 0.1%
12520 FM 1840, DE KALB, TX, 7555990
 
< 0.1%
1260 ABRAMS RD, DALLAS, TX, 7521490
 
< 0.1%
Other values (15279)279198
99.6%

Length

2021-09-22T20:49:43.710668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street53298
 
2.8%
road39564
 
2.1%
avenue31896
 
1.7%
tx26766
 
1.4%
drive25326
 
1.3%
st24066
 
1.3%
ca21312
 
1.1%
oh17172
 
0.9%
west17028
 
0.9%
north16794
 
0.9%
Other values (22664)1635984
85.7%

Most occurring characters

ValueCountFrequency (%)
1632276
 
14.4%
,845172
 
7.4%
E715644
 
6.3%
A612036
 
5.4%
R509670
 
4.5%
T501048
 
4.4%
N481626
 
4.2%
O460458
 
4.1%
0412776
 
3.6%
S394218
 
3.5%
Other values (44)4793076
42.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6403590
56.4%
Decimal Number2463408
 
21.7%
Space Separator1632276
 
14.4%
Other Punctuation856224
 
7.5%
Dash Punctuation2268
 
< 0.1%
Lowercase Letter162
 
< 0.1%
Open Punctuation36
 
< 0.1%
Close Punctuation36
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E715644
11.2%
A612036
 
9.6%
R509670
 
8.0%
T501048
 
7.8%
N481626
 
7.5%
O460458
 
7.2%
S394218
 
6.2%
L382932
 
6.0%
I368802
 
5.8%
D258084
 
4.0%
Other values (16)1719072
26.8%
Decimal Number
ValueCountFrequency (%)
0412776
16.8%
1367452
14.9%
2268290
10.9%
5250308
10.2%
3237384
9.6%
4233514
9.5%
7197838
8.0%
6197208
8.0%
8152784
 
6.2%
9145854
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
a36
22.2%
i18
11.1%
r18
11.1%
b18
11.1%
n18
11.1%
k18
11.1%
s18
11.1%
d18
11.1%
Other Punctuation
ValueCountFrequency (%)
,845172
98.7%
.9576
 
1.1%
'630
 
0.1%
#360
 
< 0.1%
/270
 
< 0.1%
&216
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1632276
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2268
100.0%
Open Punctuation
ValueCountFrequency (%)
(36
100.0%
Close Punctuation
ValueCountFrequency (%)
)36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6403752
56.4%
Common4954248
43.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E715644
11.2%
A612036
 
9.6%
R509670
 
8.0%
T501048
 
7.8%
N481626
 
7.5%
O460458
 
7.2%
S394218
 
6.2%
L382932
 
6.0%
I368802
 
5.8%
D258084
 
4.0%
Other values (24)1719234
26.8%
Common
ValueCountFrequency (%)
1632276
32.9%
,845172
17.1%
0412776
 
8.3%
1367452
 
7.4%
2268290
 
5.4%
5250308
 
5.1%
3237384
 
4.8%
4233514
 
4.7%
7197838
 
4.0%
6197208
 
4.0%
Other values (10)312030
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11358000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1632276
 
14.4%
,845172
 
7.4%
E715644
 
6.3%
A612036
 
5.4%
R509670
 
4.5%
T501048
 
4.4%
N481626
 
4.2%
O460458
 
4.1%
0412776
 
3.6%
S394218
 
3.5%
Other values (44)4793076
42.2%

Processing Date
Date

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Minimum2021-06-01 00:00:00
Maximum2021-06-01 00:00:00
2021-09-22T20:49:43.822946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:43.913388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2021-09-22T20:49:17.835520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:17.959254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:18.058501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:18.179638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:18.318554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:18.436084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:18.554923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:18.676742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:18.796237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:18.941024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:19.039485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:19.188328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:19.323742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:19.464404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:19.599831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:19.735091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:19.873040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:20.014287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:20.163279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:20.290334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:20.474441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:20.651654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:20.831270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:20.999795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:21.189685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:21.356386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:21.532717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:21.656415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:21.788733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:21.979368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:22.163136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:22.353883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:22.529920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:22.715364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:22.889558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:23.081392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:23.215481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:23.342726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:23.527338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:23.695035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:23.864641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:24.043392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:24.221527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:24.392871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:24.565534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:24.688526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:24.814879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:25.001735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:25.166530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:25.357748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:25.524149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:25.703047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:25.867312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:26.044601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:26.172792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:26.302622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:26.478698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:26.653006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:26.829125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:27.002209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:27.172711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:27.343847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:27.518895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:27.643513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:27.769739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:27.956214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:28.123612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:28.311343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:28.493283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:28.658016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:28.831324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:29.002434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:29.131625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:29.253908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:29.443324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:29.614299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:29.992569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:30.155785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:30.330688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:30.496480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-22T20:49:30.673298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-22T20:49:44.027781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-22T20:49:44.307060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-22T20:49:44.587221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-22T20:49:44.877984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-22T20:49:45.174726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-22T20:49:31.062169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-22T20:49:32.771410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-22T20:49:34.418840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-22T20:49:35.149841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Federal Provider NumberPenalty DatePenalty TypeFine AmountPayment Denial Start DatePayment Denial Length in DaysProvider NameProvider AddressProvider CityProvider StateProvider Zip CodeMeasure CodeMeasure DescriptionResident typeQ1 Measure ScoreFootnote for Q1 Measure ScoreQ2 Measure ScoreFootnote for Q2 Measure ScoreQ3 Measure ScoreFootnote for Q3 Measure ScoreQ4 Measure ScoreFootnote for Q4 Measure ScoreFour Quarter Average ScoreFootnote for Four Quarter Average ScoreUsed in Quality Measure Five Star RatingMeasure PeriodLocationProcessing Date
0015009NaTNaNNaNNaTNaNBURNS NURSING HOME, INC.701 MONROE STREET NWRUSSELLVILLEAL35653401Percentage of long-stay residents whose need for help with daily activities has increasedLong Stay9.375000NaN30.769230NaN7.894740NaN18.18182NaN16.993465NaNY2020Q1-2020Q4701 MONROE STREET NW, RUSSELLVILLE, AL, 356532021-06-01
1015009NaTNaNNaNNaTNaNBURNS NURSING HOME, INC.701 MONROE STREET NWRUSSELLVILLEAL35653404Percentage of long-stay residents who lose too much weightLong Stay8.823530NaN12.000000NaN12.500000NaN4.65116NaN9.154929NaNN2020Q1-2020Q4701 MONROE STREET NW, RUSSELLVILLE, AL, 356532021-06-01
2015009NaTNaNNaNNaTNaNBURNS NURSING HOME, INC.701 MONROE STREET NWRUSSELLVILLEAL35653405Percentage of low risk long-stay residents who lose control of their bowels or bladderLong StayNaN9.0NaN9.0NaN9.0NaN9.046.666669NaNN2020Q1-2020Q4701 MONROE STREET NW, RUSSELLVILLE, AL, 356532021-06-01
3015009NaTNaNNaNNaTNaNBURNS NURSING HOME, INC.701 MONROE STREET NWRUSSELLVILLEAL35653406Percentage of long-stay residents with a catheter inserted and left in their bladderLong Stay1.791499NaN3.645183NaN1.374846NaN0.00000NaN1.494553NaNY2020Q1-2020Q4701 MONROE STREET NW, RUSSELLVILLE, AL, 356532021-06-01
4015009NaTNaNNaNNaTNaNBURNS NURSING HOME, INC.701 MONROE STREET NWRUSSELLVILLEAL35653407Percentage of long-stay residents with a urinary tract infectionLong Stay0.000000NaN0.000000NaN0.000000NaN2.17391NaN0.636942NaNY2020Q1-2020Q4701 MONROE STREET NW, RUSSELLVILLE, AL, 356532021-06-01
5015009NaTNaNNaNNaTNaNBURNS NURSING HOME, INC.701 MONROE STREET NWRUSSELLVILLEAL35653408Percentage of long-stay residents who have depressive symptomsLong Stay0.000000NaN0.000000NaN0.000000NaN0.00000NaN0.000000NaNN2020Q1-2020Q4701 MONROE STREET NW, RUSSELLVILLE, AL, 356532021-06-01
6015009NaTNaNNaNNaTNaNBURNS NURSING HOME, INC.701 MONROE STREET NWRUSSELLVILLEAL35653409Percentage of long-stay residents who were physically restrainedLong Stay0.000000NaN0.000000NaN0.000000NaN0.00000NaN0.000000NaNN2020Q1-2020Q4701 MONROE STREET NW, RUSSELLVILLE, AL, 356532021-06-01
7015009NaTNaNNaNNaTNaNBURNS NURSING HOME, INC.701 MONROE STREET NWRUSSELLVILLEAL35653410Percentage of long-stay residents experiencing one or more falls with major injuryLong Stay2.380950NaN2.272730NaN2.380950NaN2.08333NaN2.272726NaNY2020Q1-2020Q4701 MONROE STREET NW, RUSSELLVILLE, AL, 356532021-06-01
8015009NaTNaNNaNNaTNaNBURNS NURSING HOME, INC.701 MONROE STREET NWRUSSELLVILLEAL35653415Percentage of long-stay residents assessed and appropriately given the pneumococcal vaccineLong Stay100.000000NaN100.000000NaN100.000000NaN100.00000NaN100.000000NaNN2020Q1-2020Q4701 MONROE STREET NW, RUSSELLVILLE, AL, 356532021-06-01
9015009NaTNaNNaNNaTNaNBURNS NURSING HOME, INC.701 MONROE STREET NWRUSSELLVILLEAL35653419Percentage of long-stay residents who received an antipsychotic medicationLong Stay10.256410NaN7.142860NaN10.000000NaN15.21739NaN10.778443NaNY2020Q1-2020Q4701 MONROE STREET NW, RUSSELLVILLE, AL, 356532021-06-01

Last rows

Federal Provider NumberPenalty DatePenalty TypeFine AmountPayment Denial Start DatePayment Denial Length in DaysProvider NameProvider AddressProvider CityProvider StateProvider Zip CodeMeasure CodeMeasure DescriptionResident typeQ1 Measure ScoreFootnote for Q1 Measure ScoreQ2 Measure ScoreFootnote for Q2 Measure ScoreQ3 Measure ScoreFootnote for Q3 Measure ScoreQ4 Measure ScoreFootnote for Q4 Measure ScoreFour Quarter Average ScoreFootnote for Four Quarter Average ScoreUsed in Quality Measure Five Star RatingMeasure PeriodLocationProcessing Date
280394686124NaTNaNNaNNaTNaNHARMONY HEALTH CENTER9820 N KENDALL DRIVEMIAMIFL33176415Percentage of long-stay residents assessed and appropriately given the pneumococcal vaccineLong Stay100.000000NaN100.000000NaN100.000000NaN100.000000NaN100.000000NaNN2020Q1-2020Q49820 N KENDALL DRIVE, MIAMI, FL, 331762021-06-01
280395686124NaTNaNNaNNaTNaNHARMONY HEALTH CENTER9820 N KENDALL DRIVEMIAMIFL33176419Percentage of long-stay residents who received an antipsychotic medicationLong Stay25.161290NaN21.518990NaN17.647060NaN19.867550NaN21.069693NaNY2020Q1-2020Q49820 N KENDALL DRIVE, MIAMI, FL, 331762021-06-01
280396686124NaTNaNNaNNaTNaNHARMONY HEALTH CENTER9820 N KENDALL DRIVEMIAMIFL33176430Percentage of short-stay residents assessed and appropriately given the pneumococcal vaccineShort Stay100.000000NaN100.000000NaN100.000000NaN99.166670NaN99.808062NaNN2020Q1-2020Q49820 N KENDALL DRIVE, MIAMI, FL, 331762021-06-01
280397686124NaTNaNNaNNaTNaNHARMONY HEALTH CENTER9820 N KENDALL DRIVEMIAMIFL33176434Percentage of short-stay residents who newly received an antipsychotic medicationShort Stay3.370790NaN4.285710NaN6.382980NaN8.928570NaN5.343511NaNY2020Q1-2020Q49820 N KENDALL DRIVE, MIAMI, FL, 331762021-06-01
280398686124NaTNaNNaNNaTNaNHARMONY HEALTH CENTER9820 N KENDALL DRIVEMIAMIFL33176451Percentage of long-stay residents whose ability to move independently worsenedLong Stay15.424086NaN29.485561NaN25.439255NaN25.277411NaN23.990931NaNY2020Q1-2020Q49820 N KENDALL DRIVE, MIAMI, FL, 331762021-06-01
280399686124NaTNaNNaNNaTNaNHARMONY HEALTH CENTER9820 N KENDALL DRIVEMIAMIFL33176452Percentage of long-stay residents who received an antianxiety or hypnotic medicationLong Stay29.007630NaN31.884060NaN30.935250NaN31.428570NaN30.839415NaNN2020Q1-2020Q49820 N KENDALL DRIVE, MIAMI, FL, 331762021-06-01
280400686124NaTNaNNaNNaTNaNHARMONY HEALTH CENTER9820 N KENDALL DRIVEMIAMIFL33176453Percentage of high risk long-stay residents with pressure ulcersLong Stay9.420290NaN9.285710NaN8.695650NaN7.299270NaN8.679926NaNY2020Q1-2020Q49820 N KENDALL DRIVE, MIAMI, FL, 331762021-06-01
280401686124NaTNaNNaNNaTNaNHARMONY HEALTH CENTER9820 N KENDALL DRIVEMIAMIFL33176454Percentage of long-stay residents assessed and appropriately given the seasonal influenza vaccineLong Stay98.837209NaN98.837209NaN98.837209NaN98.837209NaN98.837209NaNN2020Q1-2020Q49820 N KENDALL DRIVE, MIAMI, FL, 331762021-06-01
280402686124NaTNaNNaNNaTNaNHARMONY HEALTH CENTER9820 N KENDALL DRIVEMIAMIFL33176471Percentage of short-stay residents who made improvements in functionShort Stay51.717807NaN50.063450NaN30.140461NaN40.048864NaN45.613111NaNY2020Q1-2020Q49820 N KENDALL DRIVE, MIAMI, FL, 331762021-06-01
280403686124NaTNaNNaNNaTNaNHARMONY HEALTH CENTER9820 N KENDALL DRIVEMIAMIFL33176472Percentage of short-stay residents who were assessed and appropriately given the seasonal influenza vaccineShort Stay96.195652NaN96.195652NaN96.195652NaN96.195652NaN96.195652NaNN2020Q1-2020Q49820 N KENDALL DRIVE, MIAMI, FL, 331762021-06-01